Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm

• URL: http://arxiv.org/abs/2308.14652v1
• Date: Mon, 28 Aug 2023 15:34:43 GMT
• Title: Learning Visual Tracking and Reaching with Deep Reinforcement Learning on a UR10e Robotic Arm
• Authors: Colin Bellinger, Laurence Lamarche-Cliche
• Abstract summary: Reinforcement learning algorithms provide the potential to enable robots to learn optimal solutions to complete new tasks without reprogramming them. Current state-of-the-art in reinforcement learning relies on fast simulations and parallelization to achieve optimal performance. This report outlines our initial research into the application of deep reinforcement learning on an industrial UR10e robot.
• Score: 2.2168889407389445
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As technology progresses, industrial and scientific robots are increasingly being used in diverse settings. In many cases, however, programming the robot to perform such tasks is technically complex and costly. To maximize the utility of robots in industrial and scientific settings, they require the ability to quickly shift from one task to another. Reinforcement learning algorithms provide the potential to enable robots to learn optimal solutions to complete new tasks without directly reprogramming them. The current state-of-the-art in reinforcement learning, however, generally relies on fast simulations and parallelization to achieve optimal performance. These are often not possible in robotics applications. Thus, a significant amount of research is required to facilitate the efficient and safe, training and deployment of industrial and scientific reinforcement learning robots. This technical report outlines our initial research into the application of deep reinforcement learning on an industrial UR10e robot. The report describes the reinforcement learning environments created to facilitate policy learning with the UR10e, a robotic arm from Universal Robots, and presents our initial results in training deep Q-learning and proximal policy optimization agents on the developed reinforcement learning environments. Our results show that proximal policy optimization learns a better, more stable policy with less data than deep Q-learning. The corresponding code for this work is available at \url{https://github.com/cbellinger27/bendRL_reacher_tracker}

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